data-model

A guided tool for designing database structures for software, including the data objects, their relationships, and the tables that store them. It also covers migrations, which are controlled changes to a database, and repository interfaces, which define how code accesses stored data.

In plain words
What is it for?
Identifying entities and relationships, creating Mermaid diagrams, designing multi-tenant tables, generating migrations, and creating repository interfaces.
Why use it?
It gives feature requirements a consistent database design and includes tenant fields for applications that keep different customers’ data separate.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/ashtonian/llm-init/data-model
Any agent
npx skills add ashtonian/llm-init --skill data-model
Clone the repo
git clone --depth 1 https://github.com/ashtonian/llm-init

Made for: Claude Code, Codex.

Per session 11 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,340 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00011 $0.01340
Opus 5 $0.00005 $0.00670
Sonnet 5 $0.00002 $0.00268
Haiku 4.5 $0.00001 $0.00134

Measured 2d ago against content hash 79e0b811b36b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

data-model scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

templates/.claude/skills/data-model/SKILL.md · 184 lines

How it starts

The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Model Design Skill

Interactive workflow for designing database schemas, generating migrations, and creating repository interfaces for multi-tenant SaaS applications.

Workflow

Step 1: Identify Entities and Relationships

Analyze the feature requirements to identify:

  • Entities: Core data objects (e.g., User, Project, Invoice)
  • Relationships: One-to-one, one-to-many, many-to-many
  • Aggregate roots: Which entities are accessed independently vs. through a parent?
  • Value objects: Embedded data that doesn't have its own lifecycle

Output: Entity relationship diagram in Mermaid format.

erDiagram
    TENANT ||--o{ USER : has
    TENANT ||--o{ PROJECT : has
    PROJECT ||--o{ TASK : contains
    USER }o--o{ PROJECT : "member of"

Step 2: Design Tables with Multi-Tenant Columns

For each entity, design the table following .claude/rules/data-patterns.md:

Every table MUST include the base entity fields:

  • id (UUID, primary key)
  • tenant_id (UUID, NOT NULL, foreign key to tenants)
  • created_at (TIMESTAMPTZ, NOT NULL, DEFAULT now())
  • updated_at (TIMESTAMPTZ, NOT NULL, DEFAULT now())
  • deleted_at (TIMESTAMPTZ, nullable for soft delete)
  • created_by (UUID, foreign key to users)
  • updated_by (UUID, foreign key to users)

Plus entity-specific columns with proper types, constraints, and defaults.

Output: Complete table definitions with column types and constraints.

Step 3: Define Indexes and Constraints

For each table, define:

  • Primary key: UUID (default gen_random_uuid())
  • Foreign keys: All relationships with ON DELETE behavior
  • Unique constraints: Business uniqueness rules (e.g., UNIQUE(tenant_id, email))
  • Indexes: Foreign keys, common query patterns, search fields
  • Check constraints: Enum validation, range validation
  • Partial indexes: Active-only queries (WHERE deleted_at IS NULL)

Rules:

  • Every foreign key MUST have an index (Postgres doesn't auto-create them)
  • Composite indexes: most selective column first
  • Use CREATE INDEX CONCURRENTLY for large tables

Read the full file on GitHub · 184 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 184 lines · 11 tokens per session scan A 79e0b811b36b

Subscribe to this mod's changes

data-model is a skill published in the GitHub repository ashtonian/llm-init (2 stars, last pushed 6mo ago), licensed MIT. It adds 11 tokens to every session and 1,340 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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